


import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


lowerCAmelCase__ 						=					logging.getLogger(__name__)

lowerCAmelCase__ 						=					tf.data.AUTOTUNE
def 				_UpperCAmelCase							():
       _A :			Union[str, Any]							       =					argparse.ArgumentParser(description="Train a masked language model on TPU."		)
       parser.add_argument(
           "--pretrained_model_config"							,				type=UpperCamelCase__							,				default="roberta-base"							,				help="The model config to use. Note that we don\'t copy the model\'s weights, only the config!"							,				)
       parser.add_argument(
           "--tokenizer"							,				type=UpperCamelCase__							,				default="unigram-tokenizer-wikitext"							,				help="The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size."							,				)

       parser.add_argument(
           "--per_replica_batch_size"							,				type=UpperCamelCase__							,				default=8							,				help="Batch size per TPU core."							,				)

       parser.add_argument(
           "--no_tpu"							,				action="store_true"							,				help="If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances."							,				)

       parser.add_argument(
           "--tpu_name"							,				type=UpperCamelCase__							,				help="Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs."							,				default="local"							,				)

       parser.add_argument(
           "--tpu_zone"							,				type=UpperCamelCase__							,				help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes."							,				)

       parser.add_argument(
           "--gcp_project"							,				type=UpperCamelCase__							,				help="Google cloud project name. Only used for non-Colab TPU nodes."		)

       parser.add_argument(
           "--bfloat16"							,				action="store_true"							,				help="Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU."							,				)

       parser.add_argument(
           "--train_dataset"							,				type=UpperCamelCase__							,				help="Path to training dataset to load. If the path begins with `gs://`"
           " then the dataset will be loaded from a Google Cloud Storage bucket."							,				)

       parser.add_argument(
           "--shuffle_buffer_size"							,				type=UpperCamelCase__							,				default=2**18							,				help="Size of the shuffle buffer (in samples)"							,				)

       parser.add_argument(
           "--eval_dataset"							,				type=UpperCamelCase__							,				help="Path to evaluation dataset to load. If the path begins with `gs://`"
           " then the dataset will be loaded from a Google Cloud Storage bucket."							,				)

       parser.add_argument(
           "--num_epochs"							,				type=UpperCamelCase__							,				default=1							,				help="Number of epochs to train for."							,				)

       parser.add_argument(
           "--learning_rate"							,				type=UpperCamelCase__							,				default=1E-4							,				help="Learning rate to use for training."							,				)

       parser.add_argument(
           "--weight_decay_rate"							,				type=UpperCamelCase__							,				default=1E-3							,				help="Weight decay rate to use for training."							,				)

       parser.add_argument(
           "--max_length"							,				type=UpperCamelCase__							,				default=512							,				help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py"							,				)

       parser.add_argument(
           "--mlm_probability"							,				type=UpperCamelCase__							,				default=0.15							,				help="Fraction of tokens to mask during training."							,				)

       parser.add_argument("--output_dir"							,				type=UpperCamelCase__							,				required=UpperCamelCase__							,				help="Path to save model checkpoints to."		)
       parser.add_argument("--hub_model_id"							,				type=UpperCamelCase__							,				help="Model ID to upload to on the Hugging Face Hub."		)

       _A :			List[Any]							       =					parser.parse_args()
       return args
def 				_UpperCAmelCase							(UpperCamelCase__	:    str		):
       try:
              if args.tpu_name:
                     _A :			Optional[int]							       =					tf.distribute.cluster_resolver.TPUClusterResolver(
                         args.tpu_name							,				zone=args.tpu_zone							,				project=args.gcp_project		)
              else:
                     _A :			Optional[int]							       =					tf.distribute.cluster_resolver.TPUClusterResolver()
       except ValueError:
              raise RuntimeError(
                  "Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or "
                  "--gcp_project. When running on a TPU VM, use --tpu_name local."		)

       tf.config.experimental_connect_to_cluster(UpperCamelCase__		)
       tf.tpu.experimental.initialize_tpu_system(UpperCamelCase__		)

       return tpu
def 				_UpperCAmelCase							(UpperCamelCase__	:    List[str]		):
       _A :			List[str]							       =					0
       for file in file_list:
              _A :			List[str]							       =					file.split("/"		)[-1]
              _A :			List[str]							       =					re.search(r"-\d+-(\d+)\.tfrecord"							,				UpperCamelCase__		).group(1		)
              _A :			Optional[int]							       =					int(UpperCamelCase__		)
              num_samples += sample_count

       return num_samples
def 				_UpperCAmelCase							(UpperCamelCase__	:    Union[str, Any]							,				UpperCamelCase__	:    List[str]							,				UpperCamelCase__	:    Dict							,				UpperCamelCase__	:    str							,				UpperCamelCase__	:    Optional[Any]							,				UpperCamelCase__	:    Dict=None		):
       _A :			Tuple							       =					count_samples(UpperCamelCase__		)
       _A :			Any							       =					tf.data.Dataset.from_tensor_slices(UpperCamelCase__		)
       if shuffle:
              _A :			Union[str, Any]							       =					dataset.shuffle(len(UpperCamelCase__		)		)
       _A :			Any							       =					tf.data.TFRecordDataset(UpperCamelCase__							,				num_parallel_reads=UpperCamelCase__		)
       # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
       _A :			str							       =					dataset.apply(tf.data.experimental.assert_cardinality(UpperCamelCase__		)		)
       _A :			Optional[int]							       =					dataset.map(UpperCamelCase__							,				num_parallel_calls=UpperCamelCase__		)
       if shuffle:
              assert shuffle_buffer_size is not None
              _A :			Optional[int]							       =					dataset.shuffle(args.shuffle_buffer_size		)
       _A :			Any							       =					dataset.batch(UpperCamelCase__							,				drop_remainder=UpperCamelCase__		)
       _A :			List[str]							       =					dataset.map(UpperCamelCase__							,				num_parallel_calls=UpperCamelCase__		)
       _A :			Optional[Any]							       =					dataset.prefetch(UpperCamelCase__		)
       return dataset
def 				_UpperCAmelCase							(UpperCamelCase__	:    int		):
       if not args.no_tpu:
              _A :			Optional[int]							       =					initialize_tpu(UpperCamelCase__		)
              _A :			List[Any]							       =					tf.distribute.TPUStrategy(UpperCamelCase__		)
       else:
              _A :			str							       =					tf.distribute.OneDeviceStrategy(device="/gpu:0"		)

       if args.bfloataa:
              tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"		)

       _A :			Dict							       =					AutoTokenizer.from_pretrained(args.tokenizer		)
       _A :			str							       =					AutoConfig.from_pretrained(args.pretrained_model_config		)
       _A :			Union[str, Any]							       =					tokenizer.vocab_size

       _A :			Optional[Any]							       =					tf.io.gfile.glob(os.path.join(args.train_dataset							,				"*.tfrecord"		)		)
       if not training_records:
              raise ValueError(f"No .tfrecord files found in {args.train_dataset}."		)
       _A :			Dict							       =					tf.io.gfile.glob(os.path.join(args.eval_dataset							,				"*.tfrecord"		)		)
       if not eval_records:
              raise ValueError(f"No .tfrecord files found in {args.eval_dataset}."		)

       _A :			Union[str, Any]							       =					count_samples(UpperCamelCase__		)

       _A :			int							       =					num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
       _A :			Dict							       =					steps_per_epoch * args.num_epochs

       with strategy.scope():
              _A :			int							       =					TFAutoModelForMaskedLM.from_config(UpperCamelCase__		)
              model(model.dummy_inputs		)  # Pass some dummy inputs through the model to ensure all the weights are built
              _A       ,     _A :			Optional[int]							       =					create_optimizer(
                  num_train_steps=UpperCamelCase__							,				num_warmup_steps=total_train_steps // 20							,				init_lr=args.learning_rate							,				weight_decay_rate=args.weight_decay_rate							,				)

              # Transformers models compute the right loss for their task by default when labels are passed, and will
              # use this for training unless you specify your own loss function in compile().
              model.compile(optimizer=UpperCamelCase__							,				metrics=["accuracy"]		)

       def decode_fn(UpperCamelCase__	:    Tuple		):
              _A :			int							       =					{
                  "input_ids": tf.io.FixedLenFeature(dtype=tf.intaa							,				shape=(args.max_length,)		),
                  "attention_mask": tf.io.FixedLenFeature(dtype=tf.intaa							,				shape=(args.max_length,)		),
              }
              return tf.io.parse_single_example(UpperCamelCase__							,				UpperCamelCase__		)

       # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
       # use their methods in our data pipeline.
       _A :			int							       =					DataCollatorForLanguageModeling(
           tokenizer=UpperCamelCase__							,				mlm_probability=args.mlm_probability							,				mlm=UpperCamelCase__							,				return_tensors="tf"		)

       def mask_with_collator(UpperCamelCase__	:    List[str]		):
              # TF really needs an isin() function
              _A :			List[Any]							       =					(
                  ~tf.cast(batch["attention_mask"]							,				tf.bool		)
                  | (batch["input_ids"] == tokenizer.cls_token_id)
                  | (batch["input_ids"] == tokenizer.sep_token_id)
              )
              _A       ,     _A :			Optional[Any]							       =					data_collator.tf_mask_tokens(
                  batch["input_ids"]							,				vocab_size=len(UpperCamelCase__		)							,				mask_token_id=tokenizer.mask_token_id							,				special_tokens_mask=UpperCamelCase__							,				)
              return batch

       _A :			List[str]							       =					args.per_replica_batch_size * strategy.num_replicas_in_sync

       _A :			List[str]							       =					prepare_dataset(
           UpperCamelCase__							,				decode_fn=UpperCamelCase__							,				mask_fn=UpperCamelCase__							,				batch_size=UpperCamelCase__							,				shuffle=UpperCamelCase__							,				shuffle_buffer_size=args.shuffle_buffer_size							,				)

       _A :			List[str]							       =					prepare_dataset(
           UpperCamelCase__							,				decode_fn=UpperCamelCase__							,				mask_fn=UpperCamelCase__							,				batch_size=UpperCamelCase__							,				shuffle=UpperCamelCase__							,				)

       _A :			Optional[Any]							       =					[]
       if args.hub_model_id:
              callbacks.append(
                  PushToHubCallback(output_dir=args.output_dir							,				hub_model_id=args.hub_model_id							,				tokenizer=UpperCamelCase__		)		)

       model.fit(
           UpperCamelCase__							,				validation_data=UpperCamelCase__							,				epochs=args.num_epochs							,				callbacks=UpperCamelCase__							,				)

       model.save_pretrained(args.output_dir		)


if __name__ == "__main__":
    lowerCAmelCase__ 						=					parse_args()
    main(args)

'''simple docstring'''




import numpy as np






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       str:
    __lowerCamelCase            =    int(np.ceil((x_end - xa) / h							)							)
    __lowerCamelCase            =    np.zeros((n + 1,)							)
    __lowerCamelCase            =    ya
    __lowerCamelCase            =    xa

    for k in range(UpperCamelCase__							):
        __lowerCamelCase            =    f(UpperCamelCase__							,    y[k]							)
        __lowerCamelCase            =    f(x + 0.5 * h							,    y[k] + 0.5 * h * ka							)
        __lowerCamelCase            =    f(x + 0.5 * h							,    y[k] + 0.5 * h * ka							)
        __lowerCamelCase            =    f(x + h							,    y[k] + h * ka							)
        __lowerCamelCase            =    y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
        x += h

    return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
